2026-08-29 22:03:52
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Today at a glance:
📶 Marvell: Custom Silicon Breakout
📦 PDD: Growth Slows Again
✅ Intuit: DIY Price Reset
🧠 Synopsys: EDA Reaccelerates
🏗️ Autodesk: MaintainX Lands Cleanly
👔 Workday: AI Mix Jumps
🧑⚕️ Veeva: Falcon Lands Early Adopters
🖥️ Zoom: Enterprise Momentum Returns
🌈 Affirm: Growth Meets Leverage
🔐 Okta: Agent Deals Arrive
🔷 Rubrik: ARR Picks Up
🛒 Best Buy: PC Prices Do The Work
☁️ Nutanix: Hardware Workarounds Scale
🔍 Elastic: AI Penetration Jumps
🏥 HealthEquity: Margins Keep Climbing
Marvell Q2 FY27 revenue (ending August 1st) rose 37% Y/Y to a record $2.74 billion ($30 million beat), with non-GAAP EPS of $0.94 ($0.01 beat).
Data Center revenue jumped 46% to $2.17 billion, representing 79% of total sales, as demand for AI infrastructure remained exceptionally strong.
Custom silicon should bring the next leg of growth. Management expects a significant acceleration beginning in H2 FY27, followed by custom silicon revenue more than doubling in FY28 as hyperscalers increasingly design their own AI chips. Marvell also expanded its partnership with Google across AI accelerators, storage, networking, and near-memory compute, backed by a six-year warrant agreement.
Connectivity remains a major driver. Demand for optical interconnects is accelerating as AI clusters require more bandwidth, while Marvell called its CXL memory-expansion business a “home run” with deployments across multiple hyperscalers.
Marvell now expects FY27 revenue of roughly $12 billion (up from $11.5 billion previously) and raised its FY28 target to $18 billion (up from $16.5 billion). Q3 revenue guidance of $3.15 billion was also well ahead of consensus and implies another ~15% sequential jump.
Bottom Line: Marvell’s AI story is broadening from connectivity into custom compute. With custom silicon set to accelerate sharply, management is raising expectations faster than the current quarter alone would suggest. But expectations are already enormous: the stock trades over 50x forward earnings, and much of the Google opportunity through FY28 was already embedded in guidance. That helps explain why shares fell despite Marvell raising both FY27 and FY28 outlooks.
2026-08-28 20:04:23
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The company just crossed $96 billion in quarterly revenue, with growth accelerating above 100%. Management now expects revenue to grow another 70% next year, adding more than $200 billion in annual revenue. At this scale, that should sound almost implausible. Yet even that outlook is supply-constrained.
But the story is also getting more complicated. NVIDIA is helping finance the infrastructure its customers need, while some of those same customers are developing competing chips of their own.
So the debate now comes down to two questions:
How long can the AI infrastructure boom keep compounding?
How much of it can NVIDIA continue to capture?
Today at a glance:
NVIDIA’s Q2 FY27
Business highlights
Key quotes from the call
What to watch moving forward
NVIDIA’s fiscal year ends in January, so the July quarter was Q2 FY27.
Data Center revenue remains off the charts, as illustrated below.
Revenue accelerated 106% Y/Y to $96.2 billion ($4.1 billion beat).
Data Center +117% Y/Y to $89.0 billion.
Edge Computing +27% Y/Y to $7.2 billion.
Gross margin was 75% (+3pp Y/Y).
Operating margin was 66% (+5pp Y/Y).
Non-GAAP EPS was $2.22 ($0.13 beat).
Operating cash flow +57% Y/Y to $24.1 billion.
Free cash flow +59% Y/Y to $21.3 billion.
Cash and marketable securities: $99.4 billion.
Debt: $33.4 billion.
Revenue +12% Q/Q and +89% Y/Y to $108.0 billion ($3.4 billion beat).
Gross margin 74% (-1pp Q/Q).
Guidance assumes no Data Center compute revenue from China.
🚀 Growth accelerated again: Revenue growth jumped from 85% in Q1 to 106% in Q2, while Data Center accelerated from 92% to 117%. NVIDIA added nearly $15 billion of revenue sequentially, and Q3 guidance calls for another $12 billion.
☁️ Demand is broadening: Hyperscale revenue reached $48.7 billion, while AI Clouds, Industrial, and Enterprise grew even faster to $40.3 billion. NVIDIA is increasingly benefiting from neoclouds, enterprises, and sovereign AI alongside Big Tech.
🔄 Rubin is arriving without a digestion pause: Blackwell Ultra is still ramping, yet Rubin shipments have already started. So far, each new architecture is layering onto the previous one rather than creating an air pocket.
📉 Margins are finally bending: Gross margin is expected to fall from 75% in Q2 to 74% in Q3 and roughly 71%-72% in Q4, largely because of higher memory costs. The pressure appears supply-driven rather than a sign of weaker demand or pricing.
🧱 NVIDIA is locking up supply: Supply and capacity commitments jumped from $119 billion to $279 billion in three months, largely to secure memory. That's a huge vote of confidence in future demand—and a much larger commitment if the cycle eventually slows.
🔮 The outlook remains extraordinary: NVIDIA expects roughly 70% revenue growth in FY28, far above prior Wall Street expectations. If that holds, the AI infrastructure cycle is still expanding rapidly despite NVIDIA already operating at enormous scale.
Big picture: Growth accelerated, demand broadened, and Rubin is arriving before Blackwell has slowed. The main new wrinkle is that sustaining this pace is becoming more expensive, with memory costs pressuring margins and supply commitments rising rapidly.
NVIDIA is capturing more revenue from every AI factory generation.
Management estimates its revenue opportunity per gigawatt keeps increasing:
~$18 billion per gigawatt with Hopper.
~$25 billion per gigawatt with Blackwell.
And now ~$40 billion per gigawatt with Vera Rubin.
Rubin combines GPUs, CPUs, networking, and software to deliver 30x higher throughput per megawatt and 35x lower token costs than Grace Blackwell Ultra.
Production shipments have already started, with purchase orders from every major hyperscaler, AI cloud, and system OEM.
NVIDIA has invested nearly $50 billion in frontier AI labs and partnered with major financial institutions to help raise more than $500 billion of third-party capital for AI infrastructure.
It is also using credit support and take-or-pay commitments to help AI labs and neoclouds finance capacity. Critics call this circular financing. NVIDIA argues it is simply removing a capital bottleneck for customers whose demand is growing faster than their balance sheets.
Either way, NVIDIA is moving beyond selling infrastructure to helping make that infrastructure possible. There may also be a strategic benefit: financing infrastructure today can help lock in NVIDIA deployments before competing silicon reaches scale.
According to The Information, NVIDIA has agreed to acquire Hugging Face for $12.9 billion, nearly triple its 2023 valuation.
Hugging Face is one of the main hubs for developers to discover, share, and deploy open AI models, often dubbed the “GitHub of AI.” With only about $150 million in annual revenue, NVIDIA is clearly buying strategic positioning rather than near-term profits.
The logic ties directly to Jensen’s argument on the call: NVIDIA benefits whenever AI models proliferate. Open models are particularly attractive because startups and enterprises generally don’t build custom chips and overwhelmingly rely on existing compute infrastructure.
Owning Hugging Face would move NVIDIA one layer closer to developers and tighten the link between open-model adoption and its broader computing platform. The risk is that NVIDIA ownership could weaken the neutrality that helped make Hugging Face so valuable in the first place.
Check out the earnings call transcript on Fiscal.ai here.
“Whereas many of these XPUs are inference-specific chips for one cloud or one service, NVIDIA is a platform, an entire AI factory platform that spans the entire AI life cycle that you can use in any cloud. It’s in every cloud. You can run anywhere. [...] I have 100% confidence that our technology will continue to be extraordinary for them and that the economics of using our technology, whether it’s from data processing to training, to post-training, to agentic processing, our technology’s going to be extraordinary for them.”
This is Jensen’s answer to OpenAI’s Jalapeño and the broader custom-silicon threat. His argument isn’t that customers won’t build their own chips. It’s that those chips tend to optimize specific workloads, while NVIDIA’s advantage is a fungible platform spanning training, inference, networking, CPUs, and multiple clouds. The question is whether that breadth remains valuable enough to justify NVIDIA’s premium economics.
“When the world goes to agentic, fully agentic systems, you are going to have agents running all the time, working with other agents running all the time. [...] I think the most important thing that matters for the industry is that, one, AI is now doing productive and useful work. Two, AI is generating profitable tokens. Three, if we had more compute, we could generate more profitable tokens, which results in more profit for all of the services. This is the exact phase where we are at, which is the reason why everybody is leaning in.”
Today’s AI is still largely triggered by humans. Jensen believes the next phase involves millions of agents operating continuously and interacting with other agents. If that happens, inference demand stops being directly tied to human usage, and the amount of compute required could increase dramatically.
NVDA is up roughly 20% YTD, still outperforming the S&P 500.
Even after the post-earnings jump, the stock trades at only about 20x forward earnings, well below the multiple it commanded earlier in the AI boom and below the rest of US Big Tech.
The latest 13F filings for Q2 2026 showed that hedge funds are not accumulating NVDA as much as they used to. The stock remains one of the most widely held names, although many funds are still underexposed relative to its 8% weight in the S&P 500.
At that valuation, the debate is less about whether NVIDIA looks cheap today and more about how durable these extraordinary earnings can be.
In semiconductors, a low P/E can sometimes signal peak earnings rather than a bargain. The multiple may be compressed precisely because the market is questioning whether today’s profitability is sustainable. So far, that skepticism has been repeatedly proven wrong.
Here’s what I’m watching:
Custom silicon: OpenAI just published the first results for Jalapeño, its custom inference chip. The company says it delivered 1.5x-1.9x more throughput per watt and materially lower latency than the NVIDIA systems tested across several models. OpenAI still plans to use NVIDIA broadly, but Jalapeño shows that NVIDIA’s largest customers have a growing incentive to move specialized inference workloads onto their own silicon.
Circular financing: NVIDIA says AI labs receiving some form of balance-sheet support could account for roughly one-quarter of its business next year. Financing customers does not make the underlying demand fake, but it increases NVIDIA’s exposure if AI labs eventually struggle to monetize the infrastructure they are building.
Margins and supply: Memory scarcity is expected to push gross margin down before price increases help it recover next year. The question is whether NVIDIA can continue securing sufficient supply to meet extraordinary demand without sacrificing too much of its economics.
📉 The bear case is that AI infrastructure spending eventually outruns the profits it can generate, while custom silicon takes a growing share of inference.
📈 The bull case is that agentic AI keeps expanding compute demand faster than efficiency gains and competition can reduce it.
So far, NVIDIA is still winning that race.
That’s it for today!
Happy investing!
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Disclosure: I own AAPL, AMD, AMZN, GOOG, META, MSFT, and NVDA in App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members.
Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.
2026-08-27 07:53:39
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Investors have spent much of the year questioning whether AI will accelerate software growth or slowly eat into it.
On Wednesday, two of the industry’s biggest names gave them something else to think about.
CrowdStrike delivered record net new ARR and raised its outlook again. Salesforce posted its strongest new business growth in four years and pointed to organic revenue reacceleration ahead. Both stocks surged more than 10% after hours.
But the more interesting part is where the growth is showing up and what the market may have missed by lumping all software into the same basket.
Today at a glance:
🦅 CrowdStrike: What Doesn’t Kill It
☁️ Salesforce: SaaSpocalypse Pushback
For years, the CrowdStrike investment thesis has been that every new threat makes Threat Graph, the AI brain behind Falcon, smarter. The past two years suggest something similar about the company itself.
In July 2024, a faulty update caused one of the largest IT outages in history and put customer trust under extraordinary pressure. Two years later, CrowdStrike just delivered what CEO George Kurtz called the best quarter in company history.
Revenue grew 26% Y/Y to $1.47 billion ($30 million beat), marking the fifth consecutive quarter of acceleration, while non-GAAP EPS reached $0.31 ($0.02 beat).
Non-GAAP operating income jumped 46% to a record $372 million, a 25% margin, while free cash flow reached a Q2 record $377 million, up 33% Y/Y.
The GAAP picture remains less flattering, with the company still showing an operating loss margin of 2%. The main reason was stock-based compensation, which accounted for roughly 26% of revenue.
The recurring-revenue engine remains the main KPI:
Ending ARR grew 25% Y/Y to $5.84 billion, continuing a remarkably predictable trajectory.
Net new ARR reached an all-time record $333 million, up 51% Y/Y, accelerating sharply from +32% last quarter. New-logo net new ARR also hit a record, while both gross and net retention improved, showing that the acceleration extends beyond existing customers buying more modules.
Ending ARR from Flex customers surged 101% Y/Y to $2.29 billion, already approaching 40% of CrowdStrike’s total ARR. More than 935 accounts adopted Flex during the quarter.
Customers moving to Flex increased ending ARR by roughly 40% on average, while platform adoption continued to deepen.
The appeal of Flex is simple: customers commit a security budget upfront and can deploy it across Falcon modules over time, making it easier for CrowdStrike to consolidate more security workloads onto a single platform.
The Mythos moment we discussed last quarter is also becoming clearer in the numbers.
2026-08-22 22:02:05
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Today at a glance:
🛒 Walmart: Digital Outruns Stores
⚙️ Analog Devices: Grid-to-Chip Breakout
🎮 NetEase: Evergreen Games Deliver
🎯 Target: Traffic Holds Up
⛷️ Amer Sports: Wilson Joins In
💳 Klarna: GMV Reset
Walmart Q2 FY27 revenue rose 6% Y/Y to $187.9 billion ($1.1 billion beat), with adjusted EPS of $0.81 ($0.07 beat). Walmart US comps slowed to 2.6%, the weakest growth in more than six years and below the 3.7% consensus, sending shares sharply lower. Transactions still grew 1.5%.
The headline slowdown is somewhat misleading. New federal drug-pricing rules created a roughly 125 bps drag on US comps. Excluding Health & Wellness, comps grew 3.4%. Walmart also used some of its tariff refunds to cut prices on more than 11,000 items. The company continues gaining share, particularly in grocery and among higher-income households.
Meanwhile, the businesses increasingly driving Walmart’s economics remain much stronger than store sales:
Global e-commerce grew 23%.
Advertising surged 38%.
Membership fee revenue increased 17%.
Walmart US e-commerce has now grown above 20% for 10 consecutive quarters, with profitability improving as stores increasingly function as fulfillment hubs rather than simply physical retail locations.
Fuel remains a challenge, with FY27 incremental fuel costs now expected above $2 billion. Walmart is also expected to continue reinvesting tariff refunds into lower prices, contributing to Q3 adjusted EPS guidance of $0.62–$0.64, which is below consensus.
Despite that reinvestment, Walmart raised FY27 sales growth guidance to 4%–5% (from 3.5%–4.5%) and adjusted operating income growth to 7%–8.5% (from 6%–8%).
Bottom Line: Slower US comp reflects pharmacy pricing rather than lost share. The more important shift continues underneath, with e-commerce, advertising, membership, and marketplace growing far faster than traditional stores. Walmart increasingly looks less like a retailer with digital businesses attached and more like an omnichannel platform funded by retail.
2026-08-21 20:00:57
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Revenue rose 9% Y/Y to $39.6 billion, while Cloud accelerated to 45% growth, its fastest pace in more than five years. AI-related product revenue grew triple digits for the 12th consecutive quarter, and Cloud adjusted EBITA more than doubled as margins expanded.
The trade-off remains expensive (a familiar theme across Big Tech this earnings season). Alibaba spent almost $10 billion on CapEx during the quarter, up 75% Y/Y, while free cash flow was a $6.6 billion outflow. Adjusted EBITA also fell by 30% as the company continued to invest in AI infrastructure, models, and applications.
This quarter offered the clearest glimpse of the potential return on these AI investments, and management put a three-year payback timeline on its compute buildout.
Revenue breakdown:
🛒 China E-commerce: $16.3 billion, down 8%.
🛵 Quick Commerce: $7.9 billion, up 45%.
🌍 International Commerce: $6.1 billion, up 1%.
☁️ AI Cloud & Compute: $7.1 billion, up 45%.
🤖 AI Apps & Others: $4.9 billion, up 3%.
Alibaba overhauled its reporting segments this quarter. Quick Commerce now stands apart from legacy China e-commerce, while Alibaba Cloud absorbed the T-Head chip division.
The 8% Y/Y decline in China E-commerce reflected weaker marketplace activity and Alibaba’s continued pullback from some direct-sales businesses.
Overall revenue rose 9% Y/Y, but it came with another step down in profitability. Operating margin fell to 6% from 14% a year ago, while adjusted EBITA declined 30% to $4.0 billion as Alibaba kept investing across AI and commerce.
Alibaba is getting faster growth from the businesses it is funding most aggressively, but the cost of building them remains visible across margins and cash flow. The company is maintaining its 380 billion yuan (~$56 billion) three-year AI investment plan.
Alibaba Cloud delivered its strongest growth in more than five years, with revenue rising 45% Y/Y to $7.1 billion. AI-related products now account for roughly 35% of external Cloud revenue, up from 30% last quarter.
The acceleration is finally driving operating leverage. Cloud adjusted EBITA jumped 133% Y/Y to $830 million, expanding segment margins to roughly 12%, a notable milestone given how aggressively capacity is scaling.
Management says demand for AI compute still exceeds supply, which explains much of the current spending cycle. This quarter’s $10 billion in CapEx was primarily allocated to adding cloud infrastructure. AI-related revenue was already running at roughly $7.3 billion annually in the June quarter, with management expecting it to approach a $10 billion run rate this quarter.
The key question is how quickly those infrastructure investments can recoup their costs. Alibaba estimates that AI compute assets can currently reach breakeven in roughly three years, comfortably within their expected useful life. Management believes the payback period could eventually fall toward 2.5 years as utilization rises, Cloud margins improve, and more workloads shift toward Alibaba’s own chips.
That framework helps explain why Alibaba is comfortable sacrificing free cash flow today. If Cloud can sustain 40%+ growth while gradually improving margins, the current CapEx ramp can support a much larger recurring revenue base rather than becoming a permanent drag on returns.
Alibaba’s advantage is that it does not have to monetize AI through one product.
It owns increasingly large pieces of the stack:
Silicon: T-Head (in-house Zhenwu chips).
Compute: Alibaba Cloud.
Foundation models: Qwen (open-weight ecosystem).
Applications: QwenWork, enterprise agents, and consumer assistants.
Owning more of that stack should help the economics over time. T-Head’s Zhenwu chips already serve more than 650 external customers across 20+ industries via Alibaba Cloud. As more workloads move onto Alibaba-designed silicon, the company can reduce its reliance on expensive third-party accelerators and potentially capture more of the margin generated by AI demand.
Qwen provides another distribution advantage. The model family has surpassed 3 billion downloads, with more than 300,000 derivative models built on top of it. Alibaba can make the models broadly available while monetizing the resulting usage through inference, storage, and other Cloud services. Its Model-as-a-Service business has already surpassed 16 billion yuan (~$2.4 billion) in ARR.
The play is much bigger than selling chatbot subscriptions. Alibaba distributes Qwen to capture developers, converts that open-source adoption into sticky Cloud compute, and deploys custom silicon to protect gross margins. That creates a flywheel across the stack.
Alibaba now breaks out AI Labs & Applications (part of AI Apps and Other in our visual above), giving investors a cleaner view of what model training and front-end apps cost while Cloud scales.
The segment includes Alibaba’s model labs, the consumer Qwen business, and products such as QwenWork.
AI Labs and Applications Revenue reached $0.5 billion, up 16% Y/Y.
Adjusted EBITA loss widened to roughly $2.0 billion, more than four times the year-ago level.
The losses reflect heavy spending on model training, product development, and user acquisition. Alibaba is still competing aggressively for consumer and enterprise adoption, even as much of the underlying technology remains free or inexpensive to access.
Management expects losses to narrow as training becomes more efficient and commercialization expands. For now, though, the economics are very different across the stack. Cloud is already showing operating leverage, while AI applications remain firmly in investment mode.
Quick Commerce revenue surged 45% Y/Y to $7.9 billion, making it larger than Cloud this quarter. The business now includes Taobao Instant Commerce, Freshippo, and Tmall Supermarket’s on-demand operations.
On-demand delivery gives Alibaba a powerful frequency engine. Food delivery drives more frequent usage, while 30-minute delivery for groceries and everyday essentials expands order volume well beyond traditional multi-day marketplace shopping.
The economics are also improving:
Taobao Instant Commerce improved unit economics Q/Q while maintaining market share.
Higher average order values and a better mix of non-food orders helped margins.
Alibaba expects non-food volume to surpass food within the next fiscal year.
Quick Commerce is targeting overall profitability by FY29.
Management believes Quick Commerce could eventually represent around 30% of platform GMV. If that happens, the current spending would have done more than defend Alibaba against Meituan and JD.com. It would have added a much higher-frequency layer to an e-commerce business whose traditional China revenue is already mature.
Alibaba’s AI spending is still crushing free cash flow, but this quarter offered tangible evidence that the investment is creating economic value. Management’s roughly three-year payback estimate on AI compute assets is the most revealing metric.
AI Labs remains deeply loss-making, and Quick Commerce still requires substantial investment. Still, if Cloud sustains 40%+ growth with expanding margins, today’s massive CapEx could look like smart capital allocation. It is broadly the same playbook we are seeing from the US hyperscalers.
Next up: Saturday’s PRO edition, with Walmart, Target, Klarna, and more.
That’s it for today!
Stay healthy and invest on!
Disclosure: I own AMZN, BABA, GOOG, META, MSFT, and SHOP in App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members.
Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.
2026-08-18 20:05:03
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Every quarter, funds managing over $100 million must disclose their portfolios, offering a rare glimpse into the minds of elite investors.
The latest 13F filings capture portfolios as of June 30.
In Q2, the AI trade broadened beyond NVIDIA.
The biggest funds kept their core exposure to hyperscalers and leading chipmakers, but new money increasingly moved toward the rest of the AI supply chain. Taiwan Semiconductor, memory, storage, semiconductor equipment, and newer infrastructure names like Cerebras and Nebius featured prominently among top buys.
The theme also continued to spread into the physical economy. Power, industrials, materials, and infrastructure companies continued to attract capital as investors sought ways to participate in the massive data center buildout beyond GPUs.
Beyond AI, some of the quarter’s most interesting bets came from places few investors would expect.
Against that backdrop, super investors had to choose between doubling down on AI infrastructure, revisiting beaten-down growth stocks, or sticking with durable compounders.
Let’s see where the smart money leaned.
Today at a glance:
Hedge funds’ strategies
Top buys and top holdings in Q2
Fund picks that were not on your bingo card
Implications for individual investors
Before we dive into 13Fs, a quick reminder: blindly copying hedge fund trades is a terrible strategy.
Investing is like shooting 3-pointers. Even Steph Curry, the greatest shooter ever, misses more than half the time. There are no guaranteed outcomes, even for the pros.
Your behavior matters more than your portfolio. As Peter Lynch said, “Know what you own and why you own it.”
Conviction is what helps you hold through volatility. And conviction comes from doing your own work, not borrowing someone else’s.
As Ian Cassel puts it:
“You can borrow someone else’s stock ideas but you can’t borrow their conviction. […] Do the work so you know when to sell. Do the work so you can hold. Do the work so you can stand alone.”
Some limitations of 13F filings:
Omit short positions and cash reserves.
Offer a partial view, leaving out smaller funds.
Exclude non-US equities, bonds, and commodities.
Can be dated, given their submission 45 days after the quarter.
With all this said, let’s see what top funds were buying and holding in Q2.
Hedge funds are financial powerhouses known for flexible, aggressive strategies designed to beat the market.
Here’s what typically shapes their moves:
Market conditions: Long in bull markets, defensive in bear markets.
Sector trends: Shifts in regulation or consumer behavior steer capital.
Fundamentals: Strong earnings, free cash flow, and leadership matter.
Macro factors: Rates, inflation, and geopolitics influence positioning.
Quant models: Some lean on proprietary algorithms to find an edge.
Risk management: Diversification, hedging, and position sizing.
Investor sentiment: Fear and greed create mispriced opportunities.
Still, it doesn’t always work out.
The Global X Guru ETF (GURU), designed to track top hedge fund holdings, has underperformed the S&P 500 since its inception in 2012. And that comparison still leaves out the classic hedge fund fee drag.
And those fees matter. The classic “2 and 20” model (2% of assets + 20% of gains) can significantly reduce returns. It's no wonder that many individual investors are opting for simpler, lower-cost strategies.
Our partners at Fiscal.ai gather the data on Super Investors and visualize their portfolio for you. Pick your favorite investors and see how their holdings have evolved.
In early 2020, just before the COVID market turmoil, I curated a list of 20 top-performing hedge funds using TipRanks data. The selection focused on alpha relative to the S&P 500, and I also included a few funds frequently featured in my social feeds and podcast rotation. It’s not perfect, but it remains a solid directional filter.
The 10 stocks below represent nearly half of the top holdings listed:
🤖 AI infrastructure: TSM, NVDA, ASML, AMAT, MU.
☁️ Mega-cap platforms: AMZN, GOOG, META.
🚀 New IPOs: SPCX, CBRS.
Amazon and Taiwan Semiconductor are now tied as the most widely held stocks, appearing among the top five holdings of 9 of the 20 funds. Alphabet follows with eight, while NVIDIA appears in five. Microsoft, once a fixture on this list, appeared only once at the end of June after falling more than 20% YTD.
Apple and Tesla were entirely absent from the top-five holdings.
The holdings themselves don’t change dramatically from quarter to quarter, so let’s turn to the more actionable insights with the new movements in Q2.